Middle AI/ML Engineer (GenAI, AWS)
New
Remote-first culture; work on projects across LATAM, North America, and EuropeFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Fluent English (B2+)
- Experience
- 1–3 years
- Required Skills
- DockerPythonSQLMLFlowNumpyPandasTerraformAWS LambdaCloudFormationNLPComputer Vision
Requirements
- Solid grasp of supervised/unsupervised ML: algorithms, evaluation, trade-offs
- Deep learning hands-on experience: CNNs, RNNs, Transformers — training and fine-tuning
- Depth in at least one domain: NLP, Computer Vision, Recommendation, or Time Series
- Experience building LLM apps with OpenAI, Anthropic, or Hugging Face APIs
- Hands-on RAG design: chunking, embedding, retrieval, generation
- Familiarity with vector databases (OpenSearch, Pinecone, Chroma, FAISS)
- Understanding of prompt engineering and LLM evaluation
- Proficient with AI coding tools (Claude Code, Cursor, Copilot, etc.) — beyond autocomplete
- Experience building tool-using, stateful agents with an orchestration framework
- Understanding of Model Context Protocol (MCP) — consume or build MCP servers
- Can write technical specs for AI execution and review/correct AI-generated output
- Aware of agent monitoring, evaluation, and cost optimization in production
- Solid AWS: SageMaker, Lambda, S3, ECR, ECS, API Gateway
- Familiarity with Amazon Bedrock (model invocation, Knowledge Bases, Agents)
- Basic awareness of Infrastructure as Code (Terraform or CloudFormation)
- Production ML deployment experience
- Experiment tracking with MLflow, W&B, or similar
- CI/CD pipelines for ML; model monitoring and drift detection
- Advanced Python (async/await, OOP, packaging); strong pandas, NumPy, SQL
- Docker for containerized ML workloads
- 1–3 years of hands-on ML engineering experience
- At least one ML model deployed to production (or near-production)
- Team-based or client-facing project experience
- Demonstrated use of AI-assisted development tools
- Bachelor's/Master's in CS, Data Science, Math, or equivalent practical experience
Responsibilities
- Design and deliver ML pipelines from experimentation to production
- Build and optimize models — supervised, unsupervised, and generative AI
- Write clean, tested, modular Python code
- Deploy and monitor models; track performance and prevent drift
- Contribute to LLM applications: RAG systems and agent workflows
- Use AI coding tools on every task to move faster and write better code
- Use Claude Code or similar AI tools to deliver client projects
- Build with agent frameworks (Bedrock AgentCore, Strands, CrewAI, or similar)
- Integrate or build MCP servers for internal and client use
- Contribute features, bug fixes, or docs to the Provectus AI toolkit
- Mentor junior engineers and give actionable code review feedback
- Work closely with DevOps, Data Engineering, and Solutions Architects
- Share knowledge through docs, presentations, or internal workshops
- Stay current with ML research, GenAI, and agentic frameworks
- Propose process improvements and reusable ML accelerators
- Participate in architectural design and trade-off discussions
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